I am a Ph.D. student at the University of Southern California, advised by Prof. Greg Ver Steeg. I am interested in unsupervised learning, representation learning, and using machine learning for scientific advancement. More recently, I have been working on fairness (1, 2, 3) and privacy (4, 5) problems in machine learning and federated learning (6).
Before that, I worked on finding optimal strategies for Stackelberg security games with deep reinforcement learning and solving catastrophic forgetting in neural networks. Before coming to USC, I spent two years at Visa Inc., Bangalore, employed as a senior software developer, and five wonderful years at IIT Delhi, concluding with a Dual Degree (B.Tech and M.Tech) in Electrical Engineering.
We mitigate gender disparity in text generation while performing knowledge distillation by exploiting counterfactual role-reversed texts for training.
We illustrate that allowing access to model parameters can leak private information about the training set. We observed strong correlations between privacy leakage and overfitting, indicating that reducing overfitting may ensure privacy.
We show that limiting the mutual information between the representations limits any classifier's statistical parity. To this end, we propose a novel method for controlling fairness through mutual information based on contrastive information estimators.
We propose a new architecture for making predictions over 3D-MRIs prediction, which works by encoding a single 2D slice in an MRI with a deep 2D-CNN model and integrating the information from these 2D-slice encodings with permutation invariant layers.
We propose an approximate extension of fictitious play in continuous action spaces by modeling best responses with implicit density models.
Deriving inspiration from human complementary learning systems (hippocampus and neocortex), we develop a dual generative memory architecture that consolidates memory via generative replay and is capable of learning continuously from sequentially incoming tasks while averting catastrophic forgetting.
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